Fake news classification using machine learning techniques / Adib Farhan Ahmad Rashdi and Mohd Nizam Osman - Ahmad Rashdi, Adib Farhan; Osman, Mohd Nizam (College of Computing, Informatics and Media, UiTM Perlis, 2023)
The amounts of information, particularly text data, grows at an exponential rate as more and more time passes. Along with the data, our knowledge of machine learning also advances, and the additional processing power allows us to rapidly train models that are both highly sophisticated and very extensive. Recently, there has been a lot of emphasis focused on fake news across the globe. The impacts may be political, economic, organisational, or even personal. In this work, the technique of machine learning is broken down and discussed in an effort to overcome this challenge. The use of a TF-IDF vectorizer and the training of the data on three different classifiers in order to determine which one of them performs particularly well for this particular dataset of labelled news statements The ratings for accuracy, recall, and F1-score assist us in determining which model performs the most effectively.